





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 					A     (unittest.TestCase					):



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				Optional[Any]  					=    0



      def  __a			(       self       )   -> Tuple:



             '''simple docstring'''

             _snake_case   :				Any  					=    AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
             self.assertIsInstance(_UpperCAmelCase  ,	_UpperCAmelCase       )



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case   :				int  					=    Path(_UpperCAmelCase       ) / '''preprocessor_config.json'''
                    _snake_case   :				Tuple  					=    Path(_UpperCAmelCase       ) / '''config.json'''
                    json.dump(
                        {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )  ,	)
                    json.dump({'''model_type''': '''clip'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )       )

                    _snake_case   :				Tuple  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase       )
                    self.assertIsInstance(_UpperCAmelCase  ,	_UpperCAmelCase       )



      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case   :				Optional[int]  					=    Path(_UpperCAmelCase       ) / '''preprocessor_config.json'''
                    _snake_case   :				Tuple  					=    Path(_UpperCAmelCase       ) / '''config.json'''
                    json.dump(
                        {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )  ,	)
                    json.dump({'''model_type''': '''clip'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )       )

                    _snake_case   :				str  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase       )
                    self.assertIsInstance(_UpperCAmelCase  ,	_UpperCAmelCase       )



      def  __a			(       self       )   -> Optional[Any]:



             '''simple docstring'''

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case   :				Optional[int]  					=    CLIPConfig()

                    # Create a dummy config file with image_proceesor_type
                    _snake_case   :				int  					=    Path(_UpperCAmelCase       ) / '''preprocessor_config.json'''
                    _snake_case   :				Optional[int]  					=    Path(_UpperCAmelCase       ) / '''config.json'''
                    json.dump(
                        {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )  ,	)
                    json.dump({'''model_type''': '''clip'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )       )

                    # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                    _snake_case   :				Tuple  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase       ).to_dict()

                    config_dict.pop('''image_processor_type'''       )
                    _snake_case   :				Union[str, Any]  					=    CLIPImageProcessor(**_UpperCAmelCase       )

                    # save in new folder
                    model_config.save_pretrained(_UpperCAmelCase       )
                    config.save_pretrained(_UpperCAmelCase       )

                    _snake_case   :				int  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase       )

                    # make sure private variable is not incorrectly saved
                    _snake_case   :				Any  					=    json.loads(config.to_json_string()       )
                    self.assertTrue('''_processor_class''' not in dict_as_saved       )

             self.assertIsInstance(_UpperCAmelCase  ,	_UpperCAmelCase       )



      def  __a			(       self       )   -> str:



             '''simple docstring'''

             with tempfile.TemporaryDirectory() as tmpdirname:
                    _snake_case   :				Any  					=    Path(_UpperCAmelCase       ) / '''preprocessor_config.json'''
                    json.dump(
                        {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )  ,	)

                    _snake_case   :				Optional[Any]  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase       )
                    self.assertIsInstance(_UpperCAmelCase  ,	_UpperCAmelCase       )



      def  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             with self.assertRaisesRegex(
                 _UpperCAmelCase  ,	'''clip-base is not a local folder and is not a valid model identifier'''       ):
                    _snake_case   :				Any  					=    AutoImageProcessor.from_pretrained('''clip-base'''       )



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             with self.assertRaisesRegex(
                 _UpperCAmelCase  ,	R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''       ):
                    _snake_case   :				Dict  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase  ,	revision='''aaaaaa'''       )



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             with self.assertRaisesRegex(
                 _UpperCAmelCase  ,	'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''  ,	):
                    _snake_case   :				List[str]  					=    AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''       )



      def  __a			(       self       )   -> Union[str, Any]:



             '''simple docstring'''

             with self.assertRaises(_UpperCAmelCase       ):
                    _snake_case   :				Tuple  					=    AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''       )
             # If remote code is disabled, we can't load this config.
             with self.assertRaises(_UpperCAmelCase       ):
                    _snake_case   :				List[str]  					=    AutoImageProcessor.from_pretrained(
                        '''hf-internal-testing/test_dynamic_image_processor'''  ,	trust_remote_code=_UpperCAmelCase       )

             _snake_case   :				Tuple  					=    AutoImageProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_image_processor'''  ,	trust_remote_code=_UpperCAmelCase       )
             self.assertEqual(image_processor.__class__.__name__  ,	'''NewImageProcessor'''       )

             # Test image processor can be reloaded.
             with tempfile.TemporaryDirectory() as tmp_dir:
                    image_processor.save_pretrained(_UpperCAmelCase       )
                    _snake_case   :				Dict  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase  ,	trust_remote_code=_UpperCAmelCase       )
             self.assertEqual(reloaded_image_processor.__class__.__name__  ,	'''NewImageProcessor'''       )



      def  __a			(       self       )   -> Any:



             '''simple docstring'''

             try:
                    AutoConfig.register('''custom'''  ,	_UpperCAmelCase       )
                    AutoImageProcessor.register(_UpperCAmelCase  ,	_UpperCAmelCase       )
                    # Trying to register something existing in the Transformers library will raise an error
                    with self.assertRaises(_UpperCAmelCase       ):
                           AutoImageProcessor.register(_UpperCAmelCase  ,	_UpperCAmelCase       )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           _snake_case   :				Optional[int]  					=    Path(_UpperCAmelCase       ) / '''preprocessor_config.json'''
                           _snake_case   :				Union[str, Any]  					=    Path(_UpperCAmelCase       ) / '''config.json'''
                           json.dump(
                               {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )  ,	)
                           json.dump({'''model_type''': '''clip'''}  ,	open(_UpperCAmelCase  ,	'''w'''       )       )

                           _snake_case   :				Optional[int]  					=    CustomImageProcessor.from_pretrained(_UpperCAmelCase       )

                    # Now that the config is registered, it can be used as any other config with the auto-API
                    with tempfile.TemporaryDirectory() as tmp_dir:
                           image_processor.save_pretrained(_UpperCAmelCase       )
                           _snake_case   :				List[str]  					=    AutoImageProcessor.from_pretrained(_UpperCAmelCase       )
                           self.assertIsInstance(_UpperCAmelCase  ,	_UpperCAmelCase       )

             finally:
                    if "custom" in CONFIG_MAPPING._extra_content:
                           del CONFIG_MAPPING._extra_content["custom"]
                    if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



      def  __a			(       self       )   -> Optional[Any]:



             '''simple docstring'''

             class 					A     (snake_case__					):
                   _SCREAMING_SNAKE_CASE       =				True

             try:
                    AutoConfig.register('''custom'''  ,	_UpperCAmelCase       )
                    AutoImageProcessor.register(_UpperCAmelCase  ,	_UpperCAmelCase       )
                    # If remote code is not set, the default is to use local
                    _snake_case   :				Any  					=    AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''       )
                    self.assertEqual(image_processor.__class__.__name__  ,	'''NewImageProcessor'''       )
                    self.assertTrue(image_processor.is_local       )

                    # If remote code is disabled, we load the local one.
                    _snake_case   :				str  					=    AutoImageProcessor.from_pretrained(
                        '''hf-internal-testing/test_dynamic_image_processor'''  ,	trust_remote_code=_UpperCAmelCase       )
                    self.assertEqual(image_processor.__class__.__name__  ,	'''NewImageProcessor'''       )
                    self.assertTrue(image_processor.is_local       )

                    # If remote is enabled, we load from the Hub
                    _snake_case   :				Optional[Any]  					=    AutoImageProcessor.from_pretrained(
                        '''hf-internal-testing/test_dynamic_image_processor'''  ,	trust_remote_code=_UpperCAmelCase       )
                    self.assertEqual(image_processor.__class__.__name__  ,	'''NewImageProcessor'''       )
                    self.assertTrue(not hasattr(_UpperCAmelCase  ,	'''is_local'''       )       )

             finally:
                    if "custom" in CONFIG_MAPPING._extra_content:
                           del CONFIG_MAPPING._extra_content["custom"]
                    if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]











'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  None


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {
    '''camembert-base''': 5_1_2,
}

SCREAMING_SNAKE_CASE__     :						Any	=  '''▁'''

class 							a__( snake_case__     ):
			a_	: Tuple									=      VOCAB_FILES_NAMES
			a_	: Optional[int]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      CamembertTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]					,   **_UpperCAmelCase					,   )							->							str:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else mask_token

					super().__init__(
					    _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   additional_special_tokens=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =vocab_file
					snake_case__	     =False if not self.vocab_file else True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:

					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__	     =[self.cls_token_id]
					snake_case__	     =[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'						)

					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
							copyfile(self.vocab_file					,   _UpperCAmelCase						)

					return (out_vocab_file,)




